Method | Definition |
vDataFrame.acf | Computes the correlations of the input vcolumn and its lags. |
vDataFrame.chaid | Returns a CHAID (Chi-square Automatic Interaction Detector) tree. |
vDataFrame.corr | Computes the Correlation Matrix of the vDataFrame. |
vDataFrame.corr_pvalue | Computes the Correlation Coefficient of the two input vcolumns and its pvalue. |
vDataFrame.cov | Computes the Covariance Matrix of the vDataFrame. |
vDataFrame.iv_woe | Computes the Information Value (IV) Table. |
vDataFrame[].iv_woe | Computes the Information Value (IV) / Weight Of Evidence (WOE) Table. |
vDataFrame.pacf | Computes the partial correlations of the input vcolumn and its lags. |
vDataFrame.pivot_table_chi2 | Returns the chi-squared term using the pivot table of the response vColumn against the input vcolumns. |
vDataFrame.regr | Computes the Regression Matrix of the vDataFrame. |
Method | Definition |
vDataFrame.aad | Aggregates the vDataFrame using 'aad' (Average Absolute Deviation). |
vDataFrame[].aad | Aggregates the vcolumn using 'aad' (Average Absolute Deviation). |
vDataFrame.agg / aggregate | Aggregates the vDataFrame using the input functions. |
vDataFrame[].agg / aggregate | Aggregates the vcolumn using the input functions. |
vDataFrame.all | Aggregates the vDataFrame using 'bool_and'. |
vDataFrame.any | Aggregates the vDataFrame using 'bool_or'. |
vDataFrame.avg / mean | Aggregates the vDataFrame using 'avg' (Average). |
vDataFrame[].avg / mean | Aggregates the vcolumn using 'avg' (Average). |
vDataFrame.count | Aggregates the vDataFrame using a list of 'count' (Number of missing values). |
vDataFrame[].count | Aggregates the vcolumn using 'count' (Number of Missing elements). |
vDataFrame.count_percent | Aggregates the vDataFrame using a list of 'count' (the number of non-missing values) and percent (the percent of non-missing values). |
vDataFrame.describe | Aggregates the vDataFrame using multiple statistical aggregations. |
vDataFrame[].describe | Aggregates the vcolumn using multiple statistical aggregations. |
vDataFrame[].distinct | Returns the vcolumn distinct categories. |
vDataFrame.duplicated | Returns the duplicated values. |
vDataFrame.groupby | Aggregates the vDataFrame by grouping the elements. |
vDataFrame.kurt / kurtosis | Aggregates the vDataFrame using 'kurtosis'. |
vDataFrame[].kurt / kurtosis | Aggregates the vcolumn using 'kurtosis'. |
vDataFrame.mad | Aggregates the vDataFrame using 'mad' (Median Absolute Deviation). |
vDataFrame[].mad | Aggregates the vcolumn using 'mad' (Median Absolute Deviation). |
vDataFrame.max | Aggregates the vDataFrame using 'max' (Maximum). |
vDataFrame[].max | Aggregates the vcolumn using 'max' (Maximum). |
vDataFrame.median | Aggregates the vDataFrame using 'median'. |
vDataFrame[].median | Aggregates the vcolumn using 'median'. |
vDataFrame.min | Aggregates the vDataFrame using 'min' (Minimum). |
vDataFrame[].min | Aggregates the vcolumn using 'min' (Minimum). |
vDataFrame[].mode | Returns the nth most occurent element. |
vDataFrame[].nlargest | Returns the n largest vcolumn elements. |
vDataFrame[].nsmallest | Returns the n smallest vcolumn elements. |
vDataFrame.nunique | Aggregates the vDataFrame using 'unique' (cardinality). |
vDataFrame[].numh | Computes the optimal vcolumn bar width. |
vDataFrame[].nunique | Aggregates the vcolumn using 'unique' (cardinality). |
vDataFrame.prod /product | Aggregates the vDataFrame using 'product'. |
vDataFrame[].prod /product | Aggregates the vcolumn using 'product'. |
vDataFrame.quantile | Aggregates the vDataFrame using a list of 'quantiles'. |
vDataFrame[].quantile | Aggregates the vcolumn using an input 'quantile'. |
vDataFrame.score | Computes the score using the input columns and the input method. |
vDataFrame.sem | Aggregates the vDataFrame using 'sem' (Standard Error of the Mean). |
vDataFrame[].sem | Aggregates the vcolumn using 'sem' (Standard Error of the Mean). |
vDataFrame.shape | Returns the number of rows and columns of the vDataFrame. |
vDataFrame.skew / skewness | Aggregates the vDataFrame using 'skewness'. |
vDataFrame[].skew / skewness | Aggregates the vcolumn using 'skewness'. |
vDataFrame.std | Aggregates the vDataFrame using 'std' (Standard Deviation). |
vDataFrame[].std | Aggregates the vcolumn using 'std' (Standard Deviation). |
vDataFrame.sum | Aggregates the vDataFrame using 'sum'. |
vDataFrame[].sum | Aggregates the vcolumn using 'sum'. |
vDataFrame[].topk | Returns the top-k most occurent elements and their percentages of the distribution. |
vDataFrame[].value_counts | Returns the top-k most frequent elements and how often they appear. |
vDataFrame.var | Aggregates the vDataFrame using 'variance'. |
vDataFrame[].var | Aggregates the vcolumn using 'variance'. |
Method | Definition |
vDataFrame.catcol | Returns the vDataFrame categorical vcolumns based on a cardinality threshold. |
vDataFrame[].category | Returns the vcolumn category. |
vDataFrame[].ctype | Returns the vcolumn DB type. |
vDataFrame.current_relation | Returns the current vDataFrame relation. |
vDataFrame.datecol | Returns all the vDataFrame vcolumns of type date. |
vDataFrame.dtypes | Returns the different vcolumns types. |
vDataFrame[].dtype | Displays and Returns the vcolumn Data type. |
vDataFrame.empty | Returns True if the vDataFrame is empty. |
vDataFrame.expected_store_usage | Returns the vDataFrame expected store usage. |
vDataFrame.explain | Provides information on how Vertica is computing the current vDataFrame relation. |
vDataFrame.get_columns | Returns the vDataFrame vcolumns. |
vDataFrame[].get_len | Returns a new vColumn that represents the length of each element. |
vDataFrame.head | Returns the vDataFrame head. |
vDataFrame[].head | Returns the vcolumn head. |
vDataFrame.iloc | Returns a part of the vDataFrame (delimited by an offset and a limit). |
vDataFrame[].iloc | Returns a part of the vcolumn (delimited by an offset and a limit). |
vDataFrame.info | Displays information about the different vDataFrame transformations. |
vDataFrame[].isarray | Returns True if the vColumn is an array, False otherwise. |
vDataFrame[].isbool | Returns True if the vColumn is boolean, False otherwise. |
vDataFrame[].isdate | Returns True if the vcolumn category is date, False otherwise. |
vDataFrame[].isnum | Returns True if the vcolumn is numerical, False otherwise. |
vDataFrame[].isvmap | Returns True if the vColumn category is VMap, False otherwise. |
vDataFrame.memory_usage | Returns the vDataFrame memory usage. |
vDataFrame[].memory_usage | Returns the vcolumn memory usage. |
vDataFrame.numcol | Returns the vDataFrame numerical vcolumns. |
vDataFrame.tail | Returns the vDataFrame tail. |
vDataFrame[].tail | Returns the vcolumn tail. |
vDataFrame[].store_usage | Returns the vcolumn expected store usage (unit: b). |
vDataFrame.swap | Swap the two input vcolumns. |
vDataFrame.version | Returns the Vertica version. |
Method | Definition |
vDataFrame.append | Merges the vDataFrame with another vDataFrame or an input relation. |
vDataFrame.cdt | Returns the complete disjunctive table of the vDataFrame. |
vDataFrame.flat_vmap | Flatten the selected VMap. A new vDataFrame is returned. |
vDataFrame.groupby | Aggregates the vDataFrame by grouping its elements. |
vDataFrame.join | Joins the vDataFrame with another vDataFrame or an input relation. |
vDataFrame.narrow | Returns the narrow table of the vDataFrame using the input vcolumns. |
vDataFrame.pivot | Returns the pivot of the vDataFrame using the input aggregation. |
vDataFrame.polynomial_comb | Returns a vDataFrame containing the product combination of different input columns. This function is ideal for bivariate analysis. |
vDataFrame.recommend | Recommends items based on the collaborative filtering (CF) technique. |
vDataFrame.sort | Sorts the vDataFrame using the input vcolumns. |
Method | Definition |
vDataFrame.animated | Draws the animated chart. |
vDataFrame.bar | Draws the bar chart of the input vColumns based on an aggregation. |
vDataFrame[].bar | Draws the bar chart of the vColumn based on an aggregation. |
vDataFrame.boxplot | Draws the box plot of the input vColumns. |
vDataFrame[].boxplot | Draws the box plot of the vColumn. |
vDataFrame.bubble | Draws the bubble plot of the input vColumns. |
vDataFrame.contour | Draws the contour plot of the input function using the 2 input vColumns. |
vDataFrame.density | Draws the density plot of the vColumns. |
vDataFrame[].density | Draws the density plot of the vColumns. |
vDataFrame.hchart | Draws responsive charts using the Highchart API. |
vDataFrame[].geo_plot | Draws a geospatial object. |
vDataFrame.heatmap | Draws the heatmap of two input vColumns. |
vDataFrame.hexbin | Draws the hexbin of the input vColumns based on an aggregation. |
vDataFrame.hist | Draws the histogram of the input vColumns based on an aggregation. |
vDataFrame[].hist | Draws the histogram of the vColumn based on an aggregation. |
vDataFrame.outliers_plot | Draws the global outliers plot one or two columns based on their ZSCORE. |
vDataFrame.pie | Draws the nested density pie chart of the input vColumns. |
vDataFrame[].pie | Draws the pie chart of the vColumn based on an aggregation. |
vDataFrame.pivot_table | Draws the pivot table of one or two columns based on an aggregation. |
vDataFrame.plot | Draws a time series plot. |
vDataFrame[].plot | Draws the time series of the vColumn. |
vDataFrame[].range_plot | Draws the range plot of the vColumn. |
vDataFrame.scatter | Draws the scatter plot of the input vColumns. |
vDataFrame.scatter_matrix | Draws the scatter matrix of the vDataFrame. |
vDataFrame[].spider | Draws the spider plot of the input vColumn based on an aggregation. |
vDataFrame.stacked_area | Draws a time series stacked area chart. |